{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:06:03Z","timestamp":1777125963287,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1I1A3A01042506"],"award-info":[{"award-number":["2019R1I1A3A01042506"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the need for wildfire detection increases, research on wildfire smoke detection combining low-cost cameras and deep learning technology is increasing. Camera-based wildfire smoke detection is inexpensive, allowing for a quick detection, and allows a smoke to be checked by the naked eye. However, because a surveillance system must rely only on visual characteristics, it often erroneously detects fog and clouds as smoke. In this study, a combination of a You-Only-Look-Once detector and a long short-term memory (LSTM) classifier is applied to improve the performance of wildfire smoke detection by reflecting on the spatial and temporal characteristics of wildfire smoke. However, because it is necessary to lighten the heavy LSTM model for real-time smoke detection, in this paper, we propose a new method for applying the teacher\u2013student framework to deep LSTM. Through this method, a shallow student LSTM is designed to reduce the number of layers and cells constituting the LSTM model while maintaining the original deep LSTM performance. As the experimental results indicate, our proposed method achieves up to an 8.4-fold decrease in the number of parameters and a faster processing time than the teacher LSTM while maintaining a similar detection performance as deep LSTM using several state-of-the-art methods on a wildfire benchmark dataset.<\/jats:p>","DOI":"10.3390\/s20195508","type":"journal-article","created":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T08:02:58Z","timestamp":1601280178000},"page":"5508","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection"],"prefix":"10.3390","volume":"20","author":[{"given":"Mira","family":"Jeong","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Keimyung University, Daegu 42601, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MinJi","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Keimyung University, Daegu 42601, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaeyeal","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Keimyung University, Daegu 42601, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7284-0768","authenticated-orcid":false,"given":"Byoung Chul","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Keimyung University, Daegu 42601, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,25]]},"reference":[{"key":"ref_1","unstructured":"Congressional Research Service (2020, August 26). Wildfire Statistics. Available online: https:\/\/fas.org\/sgp\/crs\/misc\/IF10244.pdf."},{"key":"ref_2","unstructured":"NSW Rural Fire Service (2020, August 26). Bush Fire Bulletin, Volume 42, No. 1, Available online: https:\/\/www.rfs.nsw.gov.au\/__data\/assets\/pdf_file\/0007\/174823\/Bush-Fire-Bulletin-Vol-42-No1.pdf."},{"key":"ref_3","unstructured":"Yu, L., Wang, N., and Meng, X. (2005, January 26). Real-time forest fire detection with wireless sensor networks. Proceedings of the International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, China."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, S., Gao, D., Lin, H., and Sun, Q. (2019). Wildfire Detection Using Sound Spectrum Analysis Based on the Internet of Things. Sensors, 19.","DOI":"10.3390\/s19235093"},{"key":"ref_5","unstructured":"Li, Y., Wang, Z., and Song, Y. (2006, January 21\u201323). Wireless Sensor Network Design for Wildfire Monitoring. Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Blalack, T., Ellis, D., Long, M., Brown, C., Kemp, R., and Khan, M. (2019, January 11\u201314). Low-Power Distributed Sensor Network for Wildfire Detection. Proceedings of the SoutheastCon, Huntsville, AL, USA.","DOI":"10.1109\/SoutheastCon42311.2019.9020478"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ma, J., Li, X., and Zhang, J. (2018). Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery. Sensors, 18.","DOI":"10.3390\/s18030712"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"166","DOI":"10.3390\/ai1020010","article-title":"Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS","volume":"1","author":"Tang","year":"2020","journal-title":"AI"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Allison, R.S., Johnston, J.M., Craig, G., and Jennings, S. (2016). Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring. Sensors, 16.","DOI":"10.3390\/s16081310"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1016\/j.rse.2009.03.004","article-title":"Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data","volume":"113","author":"Wulder","year":"2009","journal-title":"Remote. Sens. Environ."},{"key":"ref_11","unstructured":"Kim, D., and Wang, Y.-F. (April, January 31). Smoke Detection in Video. Proceedings of the World Congress on Computer Science and Information Engineering, Los Angeles, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.proeng.2017.12.034","article-title":"Wildland Forest Fire Smoke Detection Based on Faster R-CNN using Synthetic Smoke Images","volume":"211","author":"Zhang","year":"2018","journal-title":"Procedia Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"154732","DOI":"10.1109\/ACCESS.2019.2946712","article-title":"An Attention Enhanced Bidirectional LSTM for Early Forest Fire Smoke Recognition","volume":"7","author":"Cao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"070901","DOI":"10.1117\/1.OE.51.7.070901","article-title":"Survey of computer vision-based natural disaster warning systems","volume":"51","author":"Ko","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s10694-009-0110-z","article-title":"Video Fire Smoke Detection Using Motion and Color Features","volume":"46","author":"Chunyu","year":"2009","journal-title":"Fire Technol."},{"key":"ref_16","unstructured":"T\u00f6reyin, B.U., Dedeo\u011flu, Y., and Cetin, A.E. (2005, January 4\u20138). Wavelet based real-time smoke detection in video. Proceedings of the 13th European Signal Processing Conference, Antalya, Turkey."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1016\/j.firesaf.2009.08.003","article-title":"Smoke detection in video using wavelets and support vector machines","volume":"44","author":"Gubbi","year":"2009","journal-title":"Fire Saf. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.firesaf.2011.01.001","article-title":"Video-based smoke detection with histogram sequence of LBP and LBPV pyramids","volume":"46","author":"Yuan","year":"2011","journal-title":"Fire Saf. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, T.-H., Yin, Y.-H., Huang, S.-F., and Ye, Y.-T. (2006, January 18\u201320). The smoke detection for early fire-alarming system base on video processing. Proceedings of the International Conference on Intelligent Information Hiding and Multimedia, Pasadena, CA, USA.","DOI":"10.1109\/IIH-MSP.2006.265033"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"017208","DOI":"10.1117\/1.OE.51.1.017208","article-title":"Wildfire smoke detection using temporospatial features and random forest classifiers","volume":"51","author":"Ko","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Frizzi, S., Kaabi, R., Bouchouicha, M., Ginoux, J.-M., Moreau, E., and Fnaiech, F. (2016, January 24\u201327). Convolutional neural network for video fire and smoke detection. Proceedings of the 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy.","DOI":"10.1109\/IECON.2016.7793196"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Xu, J., Xu, L., and Guo, H. (2016, January 30\u201331). Deep Convolutional Neural Networks for Forest Fire Detection. Proceedings of the International Forum on Management, Education and Information Technology Application, Guangzhou, China.","DOI":"10.2991\/ifmeita-16.2016.105"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tao, C., Zhang, J., and Wang, P. (2016, January 3\u20134). Smoke Detection Based on Deep Convolutional Neural Networks. Proceedings of the International Conference on Industrial Informatics\u2014Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), Wuhan, China.","DOI":"10.1109\/ICIICII.2016.0045"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18429","DOI":"10.1109\/ACCESS.2017.2747399","article-title":"A Deep Normalization and Convolutional Neural Network for Image Smoke Detection","volume":"5","author":"Yin","year":"2017","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Aslan, S., G\u00fcd\u00fckbay, U., T\u00f6reyin, B.U., and \u00c7etin, A.E. (2019, January 12\u201317). Early Wildfire Smoke Detection Based on Motion-based Geometric Image Transformation and Deep Convolutional Generative Adversarial Networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683629"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9237","DOI":"10.1109\/JIOT.2019.2896120","article-title":"Energy-Efficient Deep CNN for Smoke Detection in Foggy IoT Environment","volume":"6","author":"Khan","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.firesaf.2019.03.004","article-title":"Video smoke detection based on deep saliency network","volume":"105","author":"Xu","year":"2019","journal-title":"Fire Saf. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0166-4115(97)80111-2","article-title":"Serial Order: A Parallel Distributed Processing Approach","volume":"121","author":"Jordan","year":"1997","journal-title":"Adv. Psychol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1007\/s10694-019-00832-w","article-title":"Smoke Detection on Video Sequences Using 3D Convolutional Neural Networks","volume":"55","author":"Lin","year":"2019","journal-title":"Fire Technol."},{"key":"ref_31","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2017, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Filonenko, A., Kurnianggoro, L., and Jo, K.-H. (2017, January 27\u201329). Smoke Detection on Video Sequences Using Convolutional and Recurrent Neural Networks. Proceedings of the International Conference on Computational Collective Intelligence(ICCCI), Nicosia, Cyprus.","DOI":"10.1007\/978-3-319-67077-5_54"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kim, B., and Lee, J. (2019). A Video-Based Fire Detection Using Deep Learning Models. Appl. Sci., 9.","DOI":"10.3390\/app9142862"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_37","unstructured":"Karthy, S.V., Kumar, T.S., and Parameswaran, L. (2019, January 6\u20137). LSTM and GRU Deep Learning Architectures for Smoke Prediction System in Indoor Environment. Proceedings of the 3rd International Conference on Big Data and Cloud Computing (ICBDCC19), Tamilnadu, India."},{"key":"ref_38","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.imavis.2013.08.001","article-title":"Spatiotemporal bag-of-features for early wildfire smoke detection","volume":"31","author":"Ko","year":"2013","journal-title":"Image Vis. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_41","unstructured":"Law, H., Teng, Y., Russakovsky, O., and Deng, J. (2019). CornerNet-Lite: Efficient Keypoint Based Object Detection. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (November, January 27). CenterNet: Keypoint Triplets for Object Detection. Proceedings of the International Conference on Computer Vision (ICCV 2019), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00667"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Park, M., and Ko, B.C. (2020). Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube. Sensors, 20.","DOI":"10.3390\/s20082202"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, CA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tian, X., Zhang, J., Ma, Z., He, Y., Wei, J., Wu, P., Situ, W., Li, S., and Zhang, Y. (2017). Deep LSTM for Large Vocabulary Continuous Speech Recognition. arXiv.","DOI":"10.1109\/ICASSP.2018.8461404"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Price, R., Iso, K.-I., and Shinoda, K. (2016). Wise teachers train better DNN acoustic models. EURASIP J. Audio Speech Music Process., 10.","DOI":"10.1186\/s13636-016-0088-7"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"12415","DOI":"10.1109\/ACCESS.2019.2892425","article-title":"Fast Pedestrian Detection in Surveillance Video Based on Soft Target Training of Shallow Random Forest","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., and Xie, X. (2016). Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks. arXiv.","DOI":"10.1609\/aaai.v30i1.10451"},{"key":"ref_49","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the Knowledge in a Neural Network. arXiv."},{"key":"ref_50","unstructured":"VisiFire (2020, August 26). Computer Vision Based Fire Detection Software. Available online: http:\/\/signal.ee.bilkent.edu.tr\/VisiFire\/."},{"key":"ref_51","unstructured":"Fire & Smoke Database (2020, August 26). KMU Fire & Smoke Database. Available online: https:\/\/cvpr.kmu.ac.kr\/Dataset\/Dataset.htm."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5508\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:13:49Z","timestamp":1760177629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5508"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,25]]},"references-count":51,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20195508"],"URL":"https:\/\/doi.org\/10.3390\/s20195508","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,25]]}}}